Geospatial Flood Risk Mapping Using a Probabilistic Naive Bayes Model

Devni Prima Sari, Riry Sriningsih, Meira Parma Dewi, Azhari Syarief, Dina Agustina, Yuni Wardana

Abstract

Flood events occur recurrently in Padang City, Indonesia, generating substantial social, economic, and environmental consequences. This study aims to develop a geospatial flood vulnerability map using a probabilistic Naïve Bayes model integrated with GIS-based spatial analysis in ArcGIS. The model incorporates six key conditioning factors: rainfall, slope, soil type, landform, geology, and land use.
The Naïve Bayes classifier achieved an overall accuracy of 97.69%, indicating high predictive capability and model reliability. The resulting vulnerability map categorizes the study area into three classes—low, moderate, and high vulnerability. High-vulnerability zones are predominantly concentrated in the western part of Padang City, primarily due to low-lying topography, upstream surface runoff accumulation, and tidal influences.
This study presents a statistically grounded and computationally efficient framework that integrates probabilistic machine learning with spatial analysis for urban-scale flood vulnerability assessment. Compared to conventional deterministic approaches, the proposed method offers improved adaptability, rapid processing, and strong predictive performance. The framework provides valuable decision-support tools for flood risk mitigation, urban planning, and sustainable land management and can be applied to other flood-prone regions with comparable environmental characteristics.

 

Keywords: Naïve Bayes classifier; flood vulnerability mapping; GIS-based spatial analysis; probabilistic modeling; urban flood risk; disaster mitigation.

 

DOI https://doi.org/10.55463/issn.1674-2974.53.1.6


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BUI D.T., TSANGARATOS P., NGO P.T.T., PHAM T.D., and PHAM B.T. Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods. Science of The Total Environment. 2019 Jun 10;668:1038–1054.

DOU J., YUNUS A.P., BUI D.T., SAHANA M., CHEN C.W., ZHU Z., WANG W., and PHAM B.T. Evaluating GIS-Based Multiple Statistical Models and Data Mining for Earthquake and Rainfall-Induced Landslide Susceptibility Using the LiDAR DEM. Remote Sensing. 2019 Mar 15;11(6):638.

FALAH F., RAHMATI O., ROSTAMI M., AHMADISHARAF E., DALIAKOPOULOS I.N., and POURGHASEMI H.R. Artificial Neural Networks for Flood Susceptibility Mapping in Data-Scarce Urban Areas. Spatial Modeling in GIS and R for Earth and Environmental Sciences. 2019;323–336.

KHOSRAVI K., PHAM B.T., CHAPI K., SHIRZADI A., SHAHABI H., REVHAUG I., PRAKASH I., and BUI D.T. A comparative assessment of decision trees algorithms for flash flood susceptibility modeling at Haraz watershed, northern Iran. Science of The Total Environment. 2018 Jun 15;627:744–755.

NANDI A., MANDAL A., WILSON M., and SMITH D. Flood hazard mapping in Jamaica using principal component analysis and logistic regression. Environmental Earth Sciences. 2016 Mar 1;75(6):1–16.

AHMADLOU M., KARIMI M., ALIZADEH S., SHIRZADI A., PARVINNEJHAD D., SHAHABI H., and PANAHI M. Flood susceptibility assessment using integration of adaptive network-based fuzzy inference system (ANFIS) and biogeography-based optimization (BBO) and BAT algorithms (BA). 2018;34(11):1252–1272.

HONG H., PANAHI M., SHIRZADI A., MA T., LIU J., ZHU A.X., CHEN W., KOUGIAS I., and KAZAKIS N. Flood susceptibility assessment in Hengfeng area coupling adaptive neuro-fuzzy inference system with genetic algorithm and differential evolution. Science of The Total Environment. 2018 Apr 15;621:1124–1141.

NAIEM S., KHEDR A.E., IDREES A.M., and MARIE M.I. Enhancing the Efficiency of Gaussian Naïve Bayes Machine Learning Classifier in the Detection of DDOS in Cloud Computing. IEEE Access. 2023;11:124597–124608.

PERETZ O., KOREN M., and KOREN O. Naive Bayes classifier – An ensemble procedure for recall and precision enrichment. Engineering Applications of Artificial Intelligence. 2024 Oct;136:108972.

AMIN A., ADNAN A., and ANWAR S. An adaptive learning approach for customer churn prediction in the telecommunication industry using evolutionary computation and Naïve Bayes. Applied Soft Computing. 2023 Apr;137:110103.

SARI D.P., ROSHA M., and ROSADI D. Exploring the Discretization of Peak Ground Acceleration Variables: A Comparative Analysis of K-means and K-medoids. Philippine Journal of Science. 2024;153(5):1661–1668.

SCHONLAU M. The Naive Bayes Classifier. In: Springer; 2023. p. 143–160.

CHEN H., HU S., HUA R., and ZHAO X. Improved Naive Bayes classification algorithm for traffic risk management. EURASIP Journal on Advances in Signal Processing. 2021 Dec 22;2021(1):30.

KUMAR R., KRISHNA GOSWAMI B., MOTIRAM MHATRE S., and AGRAWAL S. Naive Bayes in Focus: A Thorough Examination of its Algorithmic Foundations and Use Cases. International Journal of Innovative Science and Research Technology (IJISRT). 2024 Jun 7;2078–2081.

MYTHILI J., DEEBESHKUMAR B., ESHWARAMOORTHY T., and AJAY J.N. Enhancing Email Spam Detection with Temporal Naive Bayes Classifier. In: 2024 International Conference on Communication, Computing and Internet of Things (IC3IoT). IEEE; 2024. p. 1–6.

K A., and HALDER S. Detection of Multilingual Spam SMS Using Naïve Bayes Classifier. In: 2023 IEEE 5th International Conference on Cybernetics, Cognition and Machine Learning Applications (ICCCMLA). IEEE; 2023. p. 89–94.

GAUR P., VASHISTHA S., and JHA P. Twitter Sentiment Analysis Using Naive Bayes-Based Machine Learning Technique. 2023;367–376.

LASE Y.Y., LUBIS A.R., ELYZA F., and SYAFLI S.A. Mental Health Sentiment Analysis on Social Media TikTok with the Naïve Bayes Algorithm. In: 2023 6th International Conference of Computer and Informatics Engineering (IC2IE). IEEE; 2023. p. 186–191.

KANDEL R., and BAROUD H. A data-driven risk assessment of Arctic maritime incidents: Using machine learning to predict incident types and identify risk factors. Reliability Engineering & System Safety. 2024 Mar;243:109779.

WANG B., CHEN Y., and LI Z. A novel Bayesian Pay-As-You-Drive insurance model with risk prediction and causal mapping. Decision Analytics Journal. 2024 Dec;13:100522.

PAJILA P.J.B., SHEENA B.G., GAYATHRI A., ASWINI J., NALINI M., and R S.S. A Comprehensive Survey on Naive Bayes Algorithm: Advantages, Limitations and Applications. In: 2023 4th International Conference on Smart Electronics and Communication (ICOSEC). IEEE; 2023. p. 1228–1234.

SARI D.P., ROSADI D., EFFENDI A.R., DANARDONO, and ROSHA M. Integrating Peak Ground Acceleration as a Damage Factor in Risk-Based Premium Rate Assessment using K-medoids Bayesian Networks. Journal of Applied Science and Engineering. 2024;28(6):1361–1369.

CHEBIL W., WEDYAN M., ALAZAB M., ALTURKI R., and ELSHAWEESH O. Improving Semantic Information Retrieval Using Multinomial Naive Bayes Classifier and Bayesian Networks. Information. 2023 May 3;14(5):272.


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